What Is AIOps?

AIOps is the use of machine learning, Big Data and automated decision-making to complete IT tasks. AIOps makes it possible to automate processes that would traditionally require significant manual intervention by humans.

The highly complex nature of today’s modern cloud-based distributed applications challenge monitoring approaches. To address this, DevOps practitioners and site reliability engineers (SREs) are increasingly exploring algorithmic IT and artificial intelligence as the means to detect and predict problems faster while prescribing automated self-healing and recovery processes. However, these goals are compromised by the need to maintain, manage and integrate a multiplicity of tools each with their own unique and separate data model.

By adopting a unified data model dynamically built using a time-journaled directed graph of attributed objects, teams have the analytical foundation upon which to collect, group, correlate and visualize more complex performance conditions spanning applications, infrastructure and networks. Flexible and open-ended, this modern approach allows every data source and condition to be analyzed and interpreted in the context of shared team goals and critical business outcomes.

Application Complexity Is the New Normal
The move toward an agile operation encompassing multi-cloud platforms, containers and distributed application architectures, combined with DevOps and continuous delivery practices, challenges the effectiveness of traditional monitoring tools and practices.

As a result, monitoring as a discipline has begun to suffer. It has become much harder to keep track of complex applications and infrastructure comprising many moving parts while meeting more aggressive performance expectations and consistently delivering a superior customer experience. In response, what used to be adequate for older three-tier application architectures (with more predictable patterns and better understood error conditions) is proving to be woefully inadequate for today’s modern software applications.

There are many factors challenging traditional monitoring:

Static to dynamic systems

Data to insights

More stakeholders

Specialists to generalists

These factors have raised the stakes considerably, and there’s renewed interest in monitoring, with much discussion around the failings of traditional approaches. Now, entire conferences are dedicated to monitoring (e.g., Monitorama, SREcon), and the number of new monitoring tools (including open source and commercial products) has increased considerably.

By leveraging the directed graph of attributed objects approach discussed in this paper, organizations can quickly visualize important relationships, app to infrastructure, plus gain the comprehensive and contextual insight needed to purposefully apply and prioritize AI and machine learning. Furthermore, since data changes are time-journaled, organizations gain a more dynamic model one that continuously surfaces key performance insights before, during and after key events, including workload, seasonal demand, configuration and architecture changes.

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